Real-World Experiences in AI Adoption
by Chris Fellows
Is AI hype or a force multiplier?
The last 30 years have seen several waves of technological transformations that have deeply altered the global landscape: the internet, global workforces, search engines, smart phones, and social media. Each of these technologies follows a similar pattern or “Gartner Hype Cycle” of innovation, widespread excitement and high expectations, disillusionment and disappointment, and “enlightenment” followed by productivity.
Artificial intelligence (AI) is the latest of these great transformations and the potential impact is profound. The International Monetary Fund (IMF) estimates that about 60% of jobs in advanced economies are exposed to AI, with nearly half of those facing negative effects on labor demand, wages, or hiring. Globally, some studies suggest AI could affect as many as 300 million jobs.
Property management poses an interesting use case for AI as it is an industry built on relationships, service, and trust, and at the same time, is extremely sensitive to costs and margins. Some of the questions many managers have are: How can AI drive efficiencies and cost reductions while not sacrificing resident and owner experience? Where does human versus AI value lie? What are the best use cases and approaches to AI implementation and adoption?
Real-world experiences from industry experts
At the recent IMN SFR Property Management Conference in Austin, I had the opportunity to sit down with several industry leaders with real-world experience implementing AI solutions during the panel discussion “AI Everything.” The panel included Chris Baumann, Lessen; Tiffany Rosenbaum, Rosenbaum Realty; Joe Polverari, PURE Property Management; and Ray Hespen, Property Meld. Together, they offered candid insights into how artificial intelligence is already reshaping property management, what challenges adoption brings, and where the industry is headed.
Use Case // Maintenance
Streamlining maintenance is a core AI use case for property management. Specific examples include predictive maintenance to flag equipment likely to fail, smart work order routing, automated and personalized resident communication, vendor and inventory optimizations, and managing resident and owner “sentiment” to increase satisfaction.
Tiffany shared her early experiences deploying AI at Rosenbaum Realty for maintenance and her latest project for call qualification. She explained that the timeline to train AI in any complex task that touches customers is much longer than anticipated. She also stressed that testing is critically important, and the best way to get testing done right is to delegate or outsource the task.
Property Meld is at the forefront of maintenance optimization and Ray made the point that a better maintenance experience can quantifiably and positively impact lease renewals. Longer lease terms means greater investment NOI for owners and reduced drag on staff for management.
Use Case // Call Qualification and Routing
For property management companies that handle hundreds or thousands of inquiries, AI-powered call systems and chatbots serve as the first line of response. They can answer routine questions, qualify maintenance requests, and route urgent issues to the right staff instantly.
Chris at Lessen underscored how vital call qualification is and shared the opportunity to realize significant gains in speed of response to drive a better resident and owner experience.
Use Case // Marketing and Lead Generation
Marketing, lead generation, sales nurturing, and conversion drive doors under management and brokerage sales. These are steps that AI can personalize and automate at scale. A single business owner can now produce an incredible amount of personalized and professional content that previously only a well-staffed marketing department could handle.
Use Case // Acquisitions
AI can provide underwriting guidance on the profitability of a given asset, estimate rehab numbers, and even suggest appliance and fixture replacements while itemizing costs. The promise of AI in acquisitions is a faster time to recognize value and make an offer at a better price.
Adoption Challenges // Navigating the Hype Curve
Joe Polverari has a unique perspective given his 30 years in SaaS technology leadership. He’s seen many of these “Hype Cycles” and shared that adoption and changing human behavior remain the biggest challenges to AI implementation. At Pure Property Management, the best AI products focus on user experience. Most property management staff are not technically savvy, and their job mostly revolves around human relationships. As such, AI implementations should be as easy to use as a common Point of Sale terminal and allow employees and managers to focus on what they do best: relationship management.
Why Testing AI Can Be So Challenging
Testing AI tools is not as straightforward as testing classical software. For example, attribution is difficult to ascertain. If a property’s resident satisfaction improves, was it because of the AI-driven call center, better maintenance scheduling, or simply a new property manager on staff? Also, AI isn’t perfect. A chatbot that mishandles one out of ten calls may still be “accurate” by industry standards, but that single bad interaction can damage trust with residents.
Finally, there is workflow disruption. Employees often need retraining to use AI effectively, and metrics may shift. Success is no longer just “time to answer” but whether the system routed the request correctly.
AI works best not as a replacement for people but as a companion that empowers them. The expert panel counseled that when choosing a use case to implement, first judge the cost of one bad AI-driven interaction against the efficiencies that the AI might drive. If that one bad interaction could cost a 50-door portfolio client, the AI might still need more time in training and testing.
The Best Way to Adopt AI
One of the biggest mistakes companies make is overthinking AI adoption. Instead of starting with the most complex and high-stakes projects, the smarter path is to begin with low-risk, high-reward tasks.
Property managers can use AI to draft and rewrite Standard Operating Procedures (SOPs), training manuals, and onboarding materials. These are areas where speed and clarity matter more than perfection. AI can generate first drafts in minutes, which staff can then edit and refine.
This approach has several advantages, including immediately boosting efficiencies, building familiarity and comfort with AI tools, and creating confidence and productivity for the staff which can reduce staff churn.
Used this way, AI empowers teams, reduces turnover, and paves the way for more complex use cases down the road. In complex-use cases, Ray cautioned that the success of AI implementations must move beyond reducing staff count and instead be measured against KPIs like reducing time to lease, lowering average invoice amounts, or increasing the speed of call response.
Framing AI only as a way to cut costs, risks overlooking its potential to drive real, measurable improvements in service and resident experience.
Final Thoughts
AI is not here to replace the human side of property management, it’s here to amplify it. By taking over routine tasks, AI frees managers to do what humans do best: build trust, resolve conflicts, and create community.
Joe had a final point that AI adoption will normalize just like the internet, search engines, social media, and mobile phones did. At that point, what will truly differentiate property managers is not whether they use AI, but how well they deliver resident and owner experiences.
For those willing to embrace it, AI is not just a tool for efficiency; it’s a powerful driver of resident satisfaction, revenue and sales growth, and long-term competitive advantage.
